#region .NET Disclaimer/Info

//===============================================================================
//
// gOODiDEA, uland.com
//===============================================================================
//
// $Header :		$  
// $Author :		$
// $Date   :		$
// $Revision:		$
// $History:		$  
//  
//===============================================================================

#endregion

#region Java

/* NeuQuant Neural-Net Quantization Algorithm
 * ------------------------------------------
 *
 * Copyright (c) 1994 Anthony Dekker
 *
 * NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994.
 * See "Kohonen neural networks for optimal colour quantization"
 * in "Network: Computation in Neural Systems" Vol. 5 (1994) pp 351-367.
 * for a discussion of the algorithm.
 *
 * Any party obtaining a copy of these files from the author, directly or
 * indirectly, is granted, free of charge, a full and unrestricted irrevocable,
 * world-wide, paid up, royalty-free, nonexclusive right and license to deal
 * in this software and documentation files (the "Software"), including without
 * limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
 * and/or sell copies of the Software, and to permit persons who receive
 * copies from any such party to do so, with the only requirement being
 * that this copyright notice remain intact.
 */

// Ported to Java 12/00 K Weiner

#endregion

using System;

namespace CloudShot.Utils.Encoding
{
  public class NeuQuant
  {
    protected static readonly int netsize = 256; /* number of colours used */
    /* four primes near 500 - assume no image has a length so large */
    /* that it is divisible by all four primes */
    protected static readonly int prime1 = 499;
    protected static readonly int prime2 = 491;
    protected static readonly int prime3 = 487;
    protected static readonly int prime4 = 503;
    protected static readonly int minpicturebytes = (3 * prime4);
    /* minimum size for input image */
    /* Program Skeleton
		   ----------------
		   [select samplefac in range 1..30]
		   [read image from input file]
		   pic = (unsigned char*) malloc(3*width*height);
		   initnet(pic,3*width*height,samplefac);
		   learn();
		   unbiasnet();
		   [write output image header, using writecolourmap(f)]
		   inxbuild();
		   write output image using inxsearch(b,g,r)      */

    /* Network Definitions
		   ------------------- */
    protected static readonly int maxnetpos = (netsize - 1);
    protected static readonly int netbiasshift = 4; /* bias for colour values */
    protected static readonly int ncycles = 100; /* no. of learning cycles */

    /* defs for freq and bias */
    protected static readonly int intbiasshift = 16; /* bias for fractions */
    protected static readonly int intbias = (1 << intbiasshift);
    protected static readonly int gammashift = 10; /* gamma = 1024 */
    protected static readonly int gamma = (1 << gammashift);
    protected static readonly int betashift = 10;
    protected static readonly int beta = (intbias >> betashift); /* beta = 1/1024 */
    protected static readonly int betagamma = (intbias << (gammashift - betashift));

    /* defs for decreasing radius factor */
    protected static readonly int initrad = (netsize >> 3); /* for 256 cols, radius starts */
    protected static readonly int radiusbiasshift = 6; /* at 32.0 biased by 6 bits */
    protected static readonly int radiusbias = (1 << radiusbiasshift);
    protected static readonly int initradius = (initrad * radiusbias); /* and decreases by a */
    protected static readonly int radiusdec = 30; /* factor of 1/30 each cycle */

    /* defs for decreasing alpha factor */
    protected static readonly int alphabiasshift = 10; /* alpha starts at 1.0 */
    protected static readonly int initalpha = (1 << alphabiasshift);

    /* radbias and alpharadbias used for radpower calculation */
    protected static readonly int radbiasshift = 8;
    protected static readonly int radbias = (1 << radbiasshift);
    protected static readonly int alpharadbshift = (alphabiasshift + radbiasshift);
    protected static readonly int alpharadbias = (1 << alpharadbshift);
    protected int alphadec; /* biased by 10 bits */

    /* Types and Global Variables
		-------------------------- */

    protected byte[] thepicture; /* the input image itself */
    protected int lengthcount; /* lengthcount = H*W*3 */

    protected int samplefac; /* sampling factor 1..30 */

    //   typedef int pixel[4];                /* BGRc */
    protected int[][] network; /* the network itself - [netsize][4] */

    protected int[] netindex = new int[256];
    /* for network lookup - really 256 */

    protected int[] bias = new int[netsize];
    /* bias and freq arrays for learning */
    protected int[] freq = new int[netsize];
    protected int[] radpower = new int[initrad];
    /* radpower for precomputation */

    /* Initialise network in range (0,0,0) to (255,255,255) and set parameters
		   ----------------------------------------------------------------------- */

    public NeuQuant(byte[] thepic, int len, int sample)
    {
      int i;
      int[] p;

      thepicture = thepic;
      lengthcount = len;
      samplefac = sample;

      network = new int[netsize][];
      for (i = 0; i < netsize; i++)
      {
        network[i] = new int[4];
        p = network[i];
        p[0] = p[1] = p[2] = (i << (netbiasshift + 8)) / netsize;
        freq[i] = intbias / netsize; /* 1/netsize */
        bias[i] = 0;
      }
    }

    public byte[] ColorMap()
    {
      byte[] map = new byte[3 * netsize];
      int[] index = new int[netsize];
      for (int i = 0; i < netsize; i++)
      {
        index[network[i][3]] = i;
      }
      int k = 0;
      for (int i = 0; i < netsize; i++)
      {
        int j = index[i];
        map[k++] = (byte)(network[j][0]);
        map[k++] = (byte)(network[j][1]);
        map[k++] = (byte)(network[j][2]);
      }
      return map;
    }

    /* Insertion sort of network and building of netindex[0..255] (to do after unbias)
		   ------------------------------------------------------------------------------- */

    public void Inxbuild()
    {
      int i, j, smallpos, smallval;
      int[] p;
      int[] q;
      int previouscol, startpos;

      previouscol = 0;
      startpos = 0;
      for (i = 0; i < netsize; i++)
      {
        p = network[i];
        smallpos = i;
        smallval = p[1]; /* index on g */
        /* find smallest in i..netsize-1 */
        for (j = i + 1; j < netsize; j++)
        {
          q = network[j];
          if (q[1] < smallval)
          {
            /* index on g */
            smallpos = j;
            smallval = q[1]; /* index on g */
          }
        }
        q = network[smallpos];
        /* swap p (i) and q (smallpos) entries */
        if (i != smallpos)
        {
          j = q[0];
          q[0] = p[0];
          p[0] = j;
          j = q[1];
          q[1] = p[1];
          p[1] = j;
          j = q[2];
          q[2] = p[2];
          p[2] = j;
          j = q[3];
          q[3] = p[3];
          p[3] = j;
        }
        /* smallval entry is now in position i */
        if (smallval != previouscol)
        {
          netindex[previouscol] = (startpos + i) >> 1;
          for (j = previouscol + 1; j < smallval; j++)
          {
            netindex[j] = i;
          }
          previouscol = smallval;
          startpos = i;
        }
      }
      netindex[previouscol] = (startpos + maxnetpos) >> 1;
      for (j = previouscol + 1; j < 256; j++)
      {
        netindex[j] = maxnetpos; /* really 256 */
      }
    }

    /* Main Learning Loop
		   ------------------ */

    public void Learn()
    {
      int i, j, b, g, r;
      int radius, rad, alpha, step, delta, samplepixels;
      byte[] p;
      int pix, lim;

      if (lengthcount < minpicturebytes)
        samplefac = 1;
      alphadec = 30 + ((samplefac - 1) / 3);
      p = thepicture;
      pix = 0;
      lim = lengthcount;
      samplepixels = lengthcount / (3 * samplefac);
      delta = samplepixels / ncycles;
      alpha = initalpha;
      radius = initradius;

      rad = radius >> radiusbiasshift;
      if (rad <= 1)
        rad = 0;
      for (i = 0; i < rad; i++)
      {
        radpower[i] = alpha * (((rad * rad - i * i) * radbias) / (rad * rad));
      }

      //fprintf(stderr,"beginning 1D learning: initial radius=%d\n", rad);

      if (lengthcount < minpicturebytes)
        step = 3;
      else if ((lengthcount % prime1) != 0)
        step = 3 * prime1;
      else
      {
        if ((lengthcount % prime2) != 0)
          step = 3 * prime2;
        else
        {
          if ((lengthcount % prime3) != 0)
            step = 3 * prime3;
          else
            step = 3 * prime4;
        }
      }

      i = 0;
      while (i < samplepixels)
      {
        b = (p[pix + 0] & 0xff) << netbiasshift;
        g = (p[pix + 1] & 0xff) << netbiasshift;
        r = (p[pix + 2] & 0xff) << netbiasshift;
        j = Contest(b, g, r);

        Altersingle(alpha, j, b, g, r);
        if (rad != 0)
          Alterneigh(rad, j, b, g, r); /* alter neighbours */

        pix += step;
        if (pix >= lim)
          pix -= lengthcount;

        i++;
        if (delta == 0)
          delta = 1;
        if (i % delta == 0)
        {
          alpha -= alpha / alphadec;
          radius -= radius / radiusdec;
          rad = radius >> radiusbiasshift;
          if (rad <= 1)
            rad = 0;
          for (j = 0; j < rad; j++)
          {
            radpower[j] = alpha * (((rad * rad - j * j) * radbias) / (rad * rad));
          }
        }
      }
      //fprintf(stderr,"finished 1D learning: readonly alpha=%f !\n",((float)alpha)/initalpha);
    }

    /* Search for BGR values 0..255 (after net is unbiased) and return colour index
		   ---------------------------------------------------------------------------- */

    public int Map(int b, int g, int r)
    {
      int i, j, dist, a, bestd;
      int[] p;
      int best;

      bestd = 1000; /* biggest possible dist is 256*3 */
      best = -1;
      i = netindex[g]; /* index on g */
      j = i - 1; /* start at netindex[g] and work outwards */

      while ((i < netsize) || (j >= 0))
      {
        if (i < netsize)
        {
          p = network[i];
          dist = p[1] - g; /* inx key */
          if (dist >= bestd)
            i = netsize; /* stop iter */
          else
          {
            i++;
            if (dist < 0)
              dist = -dist;
            a = p[0] - b;
            if (a < 0)
              a = -a;
            dist += a;
            if (dist < bestd)
            {
              a = p[2] - r;
              if (a < 0)
                a = -a;
              dist += a;
              if (dist < bestd)
              {
                bestd = dist;
                best = p[3];
              }
            }
          }
        }
        if (j >= 0)
        {
          p = network[j];
          dist = g - p[1]; /* inx key - reverse dif */
          if (dist >= bestd)
            j = -1; /* stop iter */
          else
          {
            j--;
            if (dist < 0)
              dist = -dist;
            a = p[0] - b;
            if (a < 0)
              a = -a;
            dist += a;
            if (dist < bestd)
            {
              a = p[2] - r;
              if (a < 0)
                a = -a;
              dist += a;
              if (dist < bestd)
              {
                bestd = dist;
                best = p[3];
              }
            }
          }
        }
      }
      return (best);
    }

    public byte[] Process()
    {
      Learn();
      Unbiasnet();
      Inxbuild();
      return ColorMap();
    }

    /* Unbias network to give byte values 0..255 and record position i to prepare for sort
		   ----------------------------------------------------------------------------------- */

    public void Unbiasnet()
    {
      int i;

      for (i = 0; i < netsize; i++)
      {
        network[i][0] >>= netbiasshift;
        network[i][1] >>= netbiasshift;
        network[i][2] >>= netbiasshift;
        network[i][3] = i; /* record colour no */
      }
    }

    /* Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in radpower[|i-j|]
		   --------------------------------------------------------------------------------- */

    protected void Alterneigh(int rad, int i, int b, int g, int r)
    {
      int j, k, lo, hi, a, m;
      int[] p;

      lo = i - rad;
      if (lo < -1)
        lo = -1;
      hi = i + rad;
      if (hi > netsize)
        hi = netsize;

      j = i + 1;
      k = i - 1;
      m = 1;
      while ((j < hi) || (k > lo))
      {
        a = radpower[m++];
        if (j < hi)
        {
          p = network[j++];
          try
          {
            p[0] -= (a * (p[0] - b)) / alpharadbias;
            p[1] -= (a * (p[1] - g)) / alpharadbias;
            p[2] -= (a * (p[2] - r)) / alpharadbias;
          }
          catch (Exception) {} // prevents 1.3 miscompilation
        }
        if (k > lo)
        {
          p = network[k--];
          try
          {
            p[0] -= (a * (p[0] - b)) / alpharadbias;
            p[1] -= (a * (p[1] - g)) / alpharadbias;
            p[2] -= (a * (p[2] - r)) / alpharadbias;
          }
          catch (Exception) {}
        }
      }
    }

    /* Move neuron i towards biased (b,g,r) by factor alpha
		   ---------------------------------------------------- */

    protected void Altersingle(int alpha, int i, int b, int g, int r)
    {
      /* alter hit neuron */
      int[] n = network[i];
      n[0] -= (alpha * (n[0] - b)) / initalpha;
      n[1] -= (alpha * (n[1] - g)) / initalpha;
      n[2] -= (alpha * (n[2] - r)) / initalpha;
    }

    /* Search for biased BGR values
		   ---------------------------- */

    protected int Contest(int b, int g, int r)
    {
      /* finds closest neuron (min dist) and updates freq */
      /* finds best neuron (min dist-bias) and returns position */
      /* for frequently chosen neurons, freq[i] is high and bias[i] is negative */
      /* bias[i] = gamma*((1/netsize)-freq[i]) */

      int i, dist, a, biasdist, betafreq;
      int bestpos, bestbiaspos, bestd, bestbiasd;
      int[] n;

      bestd = ~(1 << 31);
      bestbiasd = bestd;
      bestpos = -1;
      bestbiaspos = bestpos;

      for (i = 0; i < netsize; i++)
      {
        n = network[i];
        dist = n[0] - b;
        if (dist < 0)
          dist = -dist;
        a = n[1] - g;
        if (a < 0)
          a = -a;
        dist += a;
        a = n[2] - r;
        if (a < 0)
          a = -a;
        dist += a;
        if (dist < bestd)
        {
          bestd = dist;
          bestpos = i;
        }
        biasdist = dist - ((bias[i]) >> (intbiasshift - netbiasshift));
        if (biasdist < bestbiasd)
        {
          bestbiasd = biasdist;
          bestbiaspos = i;
        }
        betafreq = (freq[i] >> betashift);
        freq[i] -= betafreq;
        bias[i] += (betafreq << gammashift);
      }
      freq[bestpos] += beta;
      bias[bestpos] -= betagamma;
      return (bestbiaspos);
    }
  }
}